{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ec9056fb-ae59-4c60-9e47-6a283842294a",
   "metadata": {},
   "source": [
    "# 业务理解与数据探索\n",
    "数据挖掘流程:<br>\n",
    "理解业务与数据->准备数据->数据建模->模型评估->应用<br>\n",
    "<b>业务理解</b><br>\n",
    "<b>背景:</b><br>\n",
    "赛题以保险风控为背景，保险是重要的金融体系，对社会发展，民生保障起到重要作用。<br>\n",
    "保险欺诈近些年层出不穷，在某些险种上保险欺诈的金额已经占到了理赔金额的20%甚至更多。<br>\n",
    "对保险欺诈的识别成为保险行业中的关键应用场景。<br>\n",
    "<b>赛事任务：</b><br>\n",
    "数据集提供了之前客户索赔的车险数据，希望你能开发模型帮助公司预测哪些索赔是欺诈行为。<br>\n",
    "TODO：预测用户的车险是否为欺诈行为<br>\n",
    "<b>数据特征</b><br>\n",
    "<b>离散和连续特征 </b><br>\n",
    "从特征的取值数量的角度将其分为两种类型：离散特征和连续特征。<br>\r",
    "   （ 1）离散特征在一定区间范围内具有有限个取值，可以用整数、符号、布尔值、序数数据等表示。<br>\n",
    "   通常，标称特征、二元特征、序数特征和整数数值特征都是离散特征。例如，职工人数、设备台数、颜色、性别、年龄等。<br>\r",
    "   （ 2）连续特征可以在一定区间范围内任意取值，具有无限个取值。通常，区间标度特征和比率标度特征属于连续特征。<br>\n",
    "   例如，生产零件的规格尺寸、人体的身高和体重、污染物浓度等。<br>\n",
    "<b>数据探索-数据总览</b><br>\n",
    "import pandas as pd<br>\r",
    "df=pd.read_csv(\"xxx\")<br>\r",
    "#显示Dateframe所有列(参数设置为None代表显示所有行，也可以自行设置数字)<br>\r",
    "pd.set_option('display.max_columns',None)<br>\r",
    "#显示Dateframe所有行<br>\r",
    "pd.set_option('display.max_rows',None)<br>\r",
    "#设置Dataframe数据的显示长度，默认为50<br>\r",
    "pd.set_option('max_colwidth',200)<br>\r",
    "#禁止Dateframe自动换行(设置为Flase不自动换行，True反之)<br>\r",
    "pd.set_option('expand_frame_repr', False)<br>\n",
    "<b>数据探索-数据总览</b><br>\n",
    "<b>1. 查看数据集整体情况</b><br>\n",
    "用pandas的.head()等方法，查看数据的具体形式；<br>\n",
    "用.info()查看数据的类型和数据量；<br>\n",
    "用.describe查看数据极值、均值、方差等统计指标。<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2e3574f7-d849-47ff-aec4-3bdc2c17e870",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "train=pd.read_csv(\"train.csv\")\n",
    "#train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cafc73fa-9f8b-492c-8035-1c207eda9404",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns',None)\n",
    "pd.set_option('display.max_rows',None)\n",
    "pd.set_option('max_colwidth',200)\n",
    "pd.set_option('expand_frame_repr', False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6164202-1919-46a0-88b4-44d3aef1191f",
   "metadata": {},
   "source": [
    "<b>数据探索-数据总览</b>\n",
    "<b>1. 查看数据集整体情况</b><br>\r",
    "用pandas的.head()等方法，查看数据的具体形式；<br>\n",
    "用.info()查看数据的类型和数据量；<br>\n",
    "用.describe查看数据极值、均值、方差等统计指标。<br>\n",
    "df.shape  #查看数据规模<br>\r",
    "df.head() #查看数据前5行<br>\r",
    "df.dtypes  #查看每一列是什么类型<br>\r",
    "df.describe() #查看每一列的统计值<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "26260315-de26-4f8e-a721-407e9b0c8d08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>policy_id</th>\n",
       "      <th>age</th>\n",
       "      <th>customer_months</th>\n",
       "      <th>policy_deductable</th>\n",
       "      <th>policy_annual_premium</th>\n",
       "      <th>umbrella_limit</th>\n",
       "      <th>insured_zip</th>\n",
       "      <th>capital-gains</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>incident_hour_of_the_day</th>\n",
       "      <th>number_of_vehicles_involved</th>\n",
       "      <th>bodily_injuries</th>\n",
       "      <th>witnesses</th>\n",
       "      <th>total_claim_amount</th>\n",
       "      <th>injury_claim</th>\n",
       "      <th>property_claim</th>\n",
       "      <th>vehicle_claim</th>\n",
       "      <th>auto_year</th>\n",
       "      <th>fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.000000e+02</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>7.000000e+02</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.496250e+05</td>\n",
       "      <td>38.651429</td>\n",
       "      <td>205.298571</td>\n",
       "      <td>1147.857143</td>\n",
       "      <td>1246.554071</td>\n",
       "      <td>1.100000e+06</td>\n",
       "      <td>502796.822857</td>\n",
       "      <td>25841.777143</td>\n",
       "      <td>-26246.988571</td>\n",
       "      <td>11.642857</td>\n",
       "      <td>1.870000</td>\n",
       "      <td>0.994286</td>\n",
       "      <td>1.477143</td>\n",
       "      <td>52423.045714</td>\n",
       "      <td>7449.782857</td>\n",
       "      <td>7331.532857</td>\n",
       "      <td>37688.162857</td>\n",
       "      <td>2004.931429</td>\n",
       "      <td>0.258571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.590680e+05</td>\n",
       "      <td>9.210536</td>\n",
       "      <td>116.088397</td>\n",
       "      <td>611.579706</td>\n",
       "      <td>251.203263</td>\n",
       "      <td>2.282922e+06</td>\n",
       "      <td>74251.316608</td>\n",
       "      <td>28107.516454</td>\n",
       "      <td>28465.355105</td>\n",
       "      <td>6.964524</td>\n",
       "      <td>1.035265</td>\n",
       "      <td>0.818227</td>\n",
       "      <td>1.113472</td>\n",
       "      <td>26179.082222</td>\n",
       "      <td>4888.803365</td>\n",
       "      <td>4787.151705</td>\n",
       "      <td>18724.432173</td>\n",
       "      <td>5.983371</td>\n",
       "      <td>0.438163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.677100e+04</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>411.660000</td>\n",
       "      <td>-1.000000e+06</td>\n",
       "      <td>412997.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-109100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.431578e+05</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>1075.515000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>449293.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-51785.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>41618.750000</td>\n",
       "      <td>4282.500000</td>\n",
       "      <td>4471.000000</td>\n",
       "      <td>30332.500000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.423600e+05</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1256.730000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>468492.000000</td>\n",
       "      <td>11787.000000</td>\n",
       "      <td>-15587.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>57982.000000</td>\n",
       "      <td>6805.000000</td>\n",
       "      <td>6698.500000</td>\n",
       "      <td>41869.000000</td>\n",
       "      <td>2005.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.609460e+05</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>1416.410000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>590343.000000</td>\n",
       "      <td>51144.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>69996.500000</td>\n",
       "      <td>11301.750000</td>\n",
       "      <td>10750.500000</td>\n",
       "      <td>50450.500000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.045409e+06</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2004.590000</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>649422.000000</td>\n",
       "      <td>98289.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>120666.000000</td>\n",
       "      <td>21652.000000</td>\n",
       "      <td>23812.000000</td>\n",
       "      <td>78446.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          policy_id         age  customer_months  policy_deductable  policy_annual_premium  umbrella_limit    insured_zip  capital-gains   capital-loss  incident_hour_of_the_day  number_of_vehicles_involved  bodily_injuries   witnesses  total_claim_amount  injury_claim  property_claim  vehicle_claim    auto_year       fraud\n",
       "count  7.000000e+02  700.000000       700.000000         700.000000             700.000000    7.000000e+02     700.000000     700.000000     700.000000                700.000000                   700.000000       700.000000  700.000000          700.000000    700.000000      700.000000     700.000000   700.000000  700.000000\n",
       "mean   5.496250e+05   38.651429       205.298571        1147.857143            1246.554071    1.100000e+06  502796.822857   25841.777143  -26246.988571                 11.642857                     1.870000         0.994286    1.477143        52423.045714   7449.782857     7331.532857   37688.162857  2004.931429    0.258571\n",
       "std    2.590680e+05    9.210536       116.088397         611.579706             251.203263    2.282922e+06   74251.316608   28107.516454   28465.355105                  6.964524                     1.035265         0.818227    1.113472        26179.082222   4888.803365     4787.151705   18724.432173     5.983371    0.438163\n",
       "min    9.677100e+04   19.000000         0.000000         500.000000             411.660000   -1.000000e+06  412997.000000       0.000000 -109100.000000                  0.000000                     1.000000         0.000000    0.000000          100.000000      0.000000        0.000000      70.000000  1995.000000    0.000000\n",
       "25%    3.431578e+05   31.000000       114.000000         500.000000            1075.515000    0.000000e+00  449293.500000       0.000000  -51785.000000                  5.000000                     1.000000         0.000000    0.000000        41618.750000   4282.500000     4471.000000   30332.500000  2000.000000    0.000000\n",
       "50%    5.423600e+05   38.000000       202.000000        1000.000000            1256.730000    0.000000e+00  468492.000000   11787.000000  -15587.000000                 12.000000                     1.000000         1.000000    1.000000        57982.000000   6805.000000     6698.500000   41869.000000  2005.000000    0.000000\n",
       "75%    7.609460e+05   44.000000       279.000000        2000.000000            1416.410000    0.000000e+00  590343.000000   51144.500000       0.000000                 17.000000                     3.000000         2.000000    2.000000        69996.500000  11301.750000    10750.500000   50450.500000  2010.000000    1.000000\n",
       "max    1.045409e+06   63.000000       498.000000        2000.000000            2004.590000    1.000000e+07  649422.000000   98289.000000       0.000000                 23.000000                     4.000000         2.000000    3.000000       120666.000000  21652.000000    23812.000000   78446.000000  2015.000000    1.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape\n",
    "train.head()\n",
    "train.dtypes\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "177bfe86-b8a1-4b7a-9127-e5674ca90a59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "policy_state\n",
      "A    249\n",
      "B    237\n",
      "C    214\n",
      "Name: count, dtype: int64\n",
      "policy_csl\n",
      "100/300     259\n",
      "250/500     248\n",
      "500/1000    193\n",
      "Name: count, dtype: int64\n",
      "policy_deductable\n",
      "1000    250\n",
      "500     231\n",
      "2000    219\n",
      "Name: count, dtype: int64\n",
      "insured_sex\n",
      "FEMALE    370\n",
      "MALE      330\n",
      "Name: count, dtype: int64\n",
      "insured_education_level\n",
      "JD             120\n",
      "High School    105\n",
      "Associate      105\n",
      "Masters         99\n",
      "MD              93\n",
      "College         91\n",
      "PhD             87\n",
      "Name: count, dtype: int64\n",
      "insured_relationship\n",
      "own-child         130\n",
      "other-relative    126\n",
      "husband           125\n",
      "not-in-family     123\n",
      "wife              105\n",
      "unmarried          91\n",
      "Name: count, dtype: int64\n",
      "incident_type\n",
      "Multi-vehicle Collision     302\n",
      "Single Vehicle Collision    275\n",
      "Vehicle Theft                68\n",
      "Parked Car                   55\n",
      "Name: count, dtype: int64\n",
      "collision_type\n",
      "Rear Collision     208\n",
      "Side Collision     194\n",
      "Front Collision    175\n",
      "?                  123\n",
      "Name: count, dtype: int64\n",
      "incident_severity\n",
      "Minor Damage      237\n",
      "Major Damage      200\n",
      "Total Loss        199\n",
      "Trivial Damage     64\n",
      "Name: count, dtype: int64\n",
      "authorities_contacted\n",
      "Police       199\n",
      "Fire         153\n",
      "Ambulance    145\n",
      "Other        143\n",
      "Name: count, dtype: int64\n",
      "incident_state\n",
      "S1    187\n",
      "S2    178\n",
      "S3    149\n",
      "S5     78\n",
      "S4     69\n",
      "S6     21\n",
      "S7     18\n",
      "Name: count, dtype: int64\n",
      "incident_city\n",
      "Springfield    117\n",
      "Arlington      110\n",
      "Columbus       108\n",
      "Northbend       96\n",
      "Hillsdale       96\n",
      "Riverwood       90\n",
      "Northbrook      83\n",
      "Name: count, dtype: int64\n",
      "number_of_vehicles_involved\n",
      "1    398\n",
      "3    251\n",
      "4     28\n",
      "2     23\n",
      "Name: count, dtype: int64\n",
      "property_damage\n",
      "?      259\n",
      "NO     223\n",
      "YES    218\n",
      "Name: count, dtype: int64\n",
      "bodily_injuries\n",
      "0    236\n",
      "2    232\n",
      "1    232\n",
      "Name: count, dtype: int64\n",
      "witnesses\n",
      "1    181\n",
      "0    177\n",
      "2    173\n",
      "3    169\n",
      "Name: count, dtype: int64\n",
      "police_report_available\n",
      "?      247\n",
      "NO     236\n",
      "YES    217\n",
      "Name: count, dtype: int64\n",
      "fraud\n",
      "0    519\n",
      "1    181\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#查看部分字段取值分布\n",
    "for col in train.columns:\n",
    "    unique_count=train[col].nunique()\n",
    "    if(unique_count<=10):\n",
    "        print(train[col].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fd425c0-aec8-4386-9ff6-23bc9599c62d",
   "metadata": {},
   "source": [
    "<b>数据探索-查看数据缺失情况</b><br>\n",
    "<b>2. 查看数据缺失情况</b><br>\r",
    "由于数据采集设备、传输线路故障等机械原因或者记录失误等认为原因，数据缺失通常难以避免，造成缺失的原因主要有以下几种：\r",
    "- 数据暂时无法获取\r",
    "- 数据在采集过程中被遗漏或丢弃\r",
    "- 某些对象的部分特征值不存在\r",
    "- 获取数据比较困难\r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ade2d62c-0845-40e1-bf2d-772ad4c5948d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "policy_id                       0\n",
       "age                             0\n",
       "customer_months                 0\n",
       "policy_bind_date                0\n",
       "policy_state                    0\n",
       "policy_csl                      0\n",
       "policy_deductable               0\n",
       "policy_annual_premium           0\n",
       "umbrella_limit                  0\n",
       "insured_zip                     0\n",
       "insured_sex                     0\n",
       "insured_education_level         0\n",
       "insured_occupation              0\n",
       "insured_hobbies                 0\n",
       "insured_relationship            0\n",
       "capital-gains                   0\n",
       "capital-loss                    0\n",
       "incident_date                   0\n",
       "incident_type                   0\n",
       "collision_type                  0\n",
       "incident_severity               0\n",
       "authorities_contacted          60\n",
       "incident_state                  0\n",
       "incident_city                   0\n",
       "incident_hour_of_the_day        0\n",
       "number_of_vehicles_involved     0\n",
       "property_damage                 0\n",
       "bodily_injuries                 0\n",
       "witnesses                       0\n",
       "police_report_available         0\n",
       "total_claim_amount              0\n",
       "injury_claim                    0\n",
       "property_claim                  0\n",
       "vehicle_claim                   0\n",
       "auto_make                       0\n",
       "auto_model                      0\n",
       "auto_year                       0\n",
       "fraud                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "444cf889-f1d1-4e37-bfe3-34332439e191",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>missing_rate</th>\n",
       "      <th>nunique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>policy_id</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_months</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>policy_bind_date</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>policy_state</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>policy_csl</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>policy_deductable</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>policy_annual_premium</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>umbrella_limit</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>insured_zip</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>insured_sex</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>insured_education_level</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>insured_occupation</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>insured_hobbies</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>insured_relationship</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capital-gains</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capital-loss</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>incident_date</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>incident_type</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collision_type</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>incident_severity</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>authorities_contacted</th>\n",
       "      <td>640</td>\n",
       "      <td>0.085714</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>incident_state</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>incident_city</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>incident_hour_of_the_day</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_of_vehicles_involved</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>property_damage</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bodily_injuries</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>witnesses</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>police_report_available</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_claim_amount</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>injury_claim</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>property_claim</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vehicle_claim</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auto_make</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auto_model</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auto_year</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fraud</th>\n",
       "      <td>700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              count  missing_rate   nunique\n",
       "policy_id                       700      0.000000       700\n",
       "age                             700      0.000000        45\n",
       "customer_months                 700      0.000000       332\n",
       "policy_bind_date                700      0.000000       674\n",
       "policy_state                    700      0.000000         3\n",
       "policy_csl                      700      0.000000         3\n",
       "policy_deductable               700      0.000000         3\n",
       "policy_annual_premium           700      0.000000       698\n",
       "umbrella_limit                  700      0.000000        11\n",
       "insured_zip                     700      0.000000       699\n",
       "insured_sex                     700      0.000000         2\n",
       "insured_education_level         700      0.000000         7\n",
       "insured_occupation              700      0.000000        14\n",
       "insured_hobbies                 700      0.000000        20\n",
       "insured_relationship            700      0.000000         6\n",
       "capital-gains                   700      0.000000       352\n",
       "capital-loss                    700      0.000000       357\n",
       "incident_date                   700      0.000000       112\n",
       "incident_type                   700      0.000000         4\n",
       "collision_type                  700      0.000000         4\n",
       "incident_severity               700      0.000000         4\n",
       "authorities_contacted           640      0.085714         4\n",
       "incident_state                  700      0.000000         7\n",
       "incident_city                   700      0.000000         7\n",
       "incident_hour_of_the_day        700      0.000000        24\n",
       "number_of_vehicles_involved     700      0.000000         4\n",
       "property_damage                 700      0.000000         3\n",
       "bodily_injuries                 700      0.000000         3\n",
       "witnesses                       700      0.000000         4\n",
       "police_report_available         700      0.000000         3\n",
       "total_claim_amount              700      0.000000       694\n",
       "injury_claim                    700      0.000000       665\n",
       "property_claim                  700      0.000000       662\n",
       "vehicle_claim                   700      0.000000       697\n",
       "auto_make                       700      0.000000        14\n",
       "auto_model                      700      0.000000        39\n",
       "auto_year                       700      0.000000        21\n",
       "fraud                           700      0.000000         2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看每个字段的缺失值、唯一值个数\n",
    "tmp = pd.DataFrame()\n",
    "cols = train.columns\n",
    "tmp[ ' count'] = train[ cols ].count().values\n",
    "tmp[ 'missing_rate'] = (train.shape[0] - tmp[ ' count']) / train.shape[0]\n",
    "tmp[ ' nunique'] = train[ cols ].nunique().values\n",
    "tmp.index = cols\n",
    "tmp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6fde0c1-d1a5-48ce-840d-d9655cc2cea4",
   "metadata": {},
   "source": [
    "<b>数据探索-查看数据异常情况</b><br>\n",
    "<b>3.查看数据异常情况</b><br>\r",
    "异常值是数据集中存在的非正常的值，也成为了离群点，异常值有可能是不正确的“脏数据”，<br>\n",
    "也可能是正确的异常数据，例如人的身高100m属于不正确的脏数据<br>\n",
    "<b>异常值判别方法</b><br>\r",
    "- <b>简单统计分析</b><br>\r",
    "    对特征进行描述性统计分析，观察是否有不合理的特征值\r",
    "- <b>3σ原则</b><br>\r",
    "     如果数据服从正态分布，则与均值μ的差超过三倍标准σ的特征值，可以视为异常值，<br>\n",
    "  如果数据不服从正态分布，亦可以通过原理平均距离若干倍的标准差来判断，<br>\n",
    "  倍数的取值根据经验和实际情况确定<br>\r",
    "- <b>箱线法</b><br>\r",
    "通过数据集的四分位数形成的图形化描述来查找异常值<br>\n",
    "    \n",
    "箱线图（Box Plot）是通过数据集的四分位数形成的图形化描述，<br>\n",
    "是一种非常简单，而且有效的可视化离群点的方法<br>\r",
    "- 下四分位数：25%分位点所对应的值(Q1)\r",
    "- 中位数：50%分位点对应的值(Q2)\r",
    "- 上四分位数：75%分位点所对应的值(Q3)\r",
    "- 上须：Q3+1.5(Q3-Q1)\r",
    "- 下须：Q1-1.5(Q3-Q1)\r",
    "其中Q3-Q1表示四分位，上四分位数与下四分位数的差距又称四分位距（interquartile range, IQR）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db8ed1fc-20cf-40d7-8498-4547f8b04bd1",
   "metadata": {},
   "source": [
    "Seaborn包中的boxplot可以用于绘制箱线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "59dd5af3-7a20-4d65-87ee-ea2087021513",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 3000x3000 with 19 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#分离数值变量与分类变量\n",
    "Nu_feature = list(train.select_dtypes(exclude=[ 'object']).columns)# 数值变墅\n",
    "Ca_feature = list(train.select_dtypes(include=[ 'object']).columns)\n",
    "#绘制箱线图\n",
    "plt.figure(figsize=(30,30))#箱线图查看数值型变量异常值\n",
    "i=1\n",
    "for col in Nu_feature:\n",
    "   ax=plt.subplot(4,5,i)\n",
    "   ax=sns.boxplot(data=train[ col])\n",
    "   ax.set_xlabel(col)\n",
    "   ax.set_ylabel( 'Frequency ')\n",
    "   i+=1\n",
    "plt.show()\n",
    "#结合原饴数据及经验，真正的异常值只有umbrelLa_limit这一个变量，有一个-10000o的异常值，但测试柴没有，可以忽略不管\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aa976bc-375c-4b1b-8c1f-55a79a73384c",
   "metadata": {},
   "source": [
    "<b>数据探索-查看数据分布</b><br>\n",
    "1.可以使用value_counts()方法统计<br>\r",
    "2.也可以用柱状图展示<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "688b3f62-75ec-43ab-a4ac-82d777e9c37e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fraud\n",
       "0    519\n",
       "1    181\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['fraud'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cf5fedb1-b1d7-4223-bafe-da9c6775856d",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Axes' object has no attribute 'txt'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[10], line 7\u001b[0m\n\u001b[0;32m      4\u001b[0m     ax\u001b[38;5;241m.\u001b[39mannotate(label,(p\u001b[38;5;241m.\u001b[39mget_x()\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m0.320\u001b[39m,p\u001b[38;5;241m.\u001b[39mget_height()))\n\u001b[0;32m      6\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m欺诈行为\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m0:无欺诈行为|1:欺诈\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m----> 7\u001b[0m ax\u001b[38;5;241m.\u001b[39mtxt(\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m300\u001b[39m,\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mround\u001b[39m(\u001b[38;5;241m519\u001b[39m\u001b[38;5;241m/\u001b[39m\u001b[38;5;28mlen\u001b[39m(train),\u001b[38;5;241m2\u001b[39m)\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      8\u001b[0m ax\u001b[38;5;241m.\u001b[39mtxt(\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m300\u001b[39m,\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mround\u001b[39m(\u001b[38;5;241m181\u001b[39m\u001b[38;5;241m/\u001b[39m\u001b[38;5;28mlen\u001b[39m(train),\u001b[38;5;241m2\u001b[39m)\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      9\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Axes' object has no attribute 'txt'"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\Lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 27450 (\\N{CJK UNIFIED IDEOGRAPH-6B3A}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "D:\\Anaconda\\Lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 35784 (\\N{CJK UNIFIED IDEOGRAPH-8BC8}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "D:\\Anaconda\\Lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 34892 (\\N{CJK UNIFIED IDEOGRAPH-884C}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "D:\\Anaconda\\Lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 20026 (\\N{CJK UNIFIED IDEOGRAPH-4E3A}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "D:\\Anaconda\\Lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 26080 (\\N{CJK UNIFIED IDEOGRAPH-65E0}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "D:\\Anaconda\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 27450 (\\N{CJK UNIFIED IDEOGRAPH-6B3A}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "D:\\Anaconda\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 35784 (\\N{CJK UNIFIED IDEOGRAPH-8BC8}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "D:\\Anaconda\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 34892 (\\N{CJK UNIFIED IDEOGRAPH-884C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "D:\\Anaconda\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 20026 (\\N{CJK UNIFIED IDEOGRAPH-4E3A}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "D:\\Anaconda\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 26080 (\\N{CJK UNIFIED IDEOGRAPH-65E0}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context({'figure.figsize':[6,6]})\n",
    "ax=sns.countplot(x='fraud',data=train)\n",
    "for p,label in zip(ax.patches,train['fraud'].value_counts().values):\n",
    "    ax.annotate(label,(p.get_x()+0.320,p.get_height()))\n",
    "\n",
    "ax.set_title('欺诈行为\\n0:无欺诈行为|1:欺诈')\n",
    "ax.txt(0,300,f'{round(519/len(train),2)*100}%')\n",
    "ax.txt(1,300,f'{round(181/len(train),2)*100}%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2570532b-5e7e-4e94-945a-ce71d50906d3",
   "metadata": {},
   "source": [
    "<b>数据探索-查看数据分布</b><br>\n",
    "数值型特征数据分布<br>\r",
    "seaborn包：Python中基于matplotlib的具有更多可视化功能和更优美绘图风格的绘图模块<br>\r",
    "seaborn中的kdeplot可用于对单变量和双变量进行核密度估计并可视化<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "59fd3548-f51e-4088-98e0-76b315f709f1",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'test' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[11], line 10\u001b[0m\n\u001b[0;32m      8\u001b[0m ax\u001b[38;5;241m=\u001b[39mplt\u001b[38;5;241m.\u001b[39msubplot(\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m5\u001b[39m,i)\n\u001b[0;32m      9\u001b[0m ax\u001b[38;5;241m=\u001b[39msns\u001b[38;5;241m.\u001b[39m kdeplot(train[ col],color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mred\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 10\u001b[0m ax\u001b[38;5;241m=\u001b[39msns\u001b[38;5;241m.\u001b[39m kdeplot(test[col],color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcyan\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     11\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_xlabel(col)\n\u001b[0;32m     12\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_ylabel( \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFrequency \u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'test' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 3000x2500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "plt.figure(figsize=( 30,25))\n",
    "i=1\n",
    "#忽路标签字段\n",
    "Nu_feature_2 = Nu_feature[ 0: -1]\n",
    "for col in Nu_feature_2:\n",
    "   ax=plt.subplot(4,5,i)\n",
    "   ax=sns. kdeplot(train[ col],color='red')\n",
    "   ax=sns. kdeplot(test[col],color='cyan')\n",
    "   ax.set_xlabel(col)\n",
    "   ax.set_ylabel( 'Frequency ')\n",
    "   ax=ax.legend([ 'train ' , 'test' ])\n",
    "   i+=1\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0eeea84-9b6e-495b-8047-594f97ddb1c4",
   "metadata": {},
   "source": [
    "<b>数据探索-查看数据分布</b><br>\n",
    "类别型特征数据分布<br>\r",
    "使用matplotlib.pyplot包中的scatter方式绘制散点图<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f662b07b-5378-412f-a90e-21e01772db24",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'policy_state '",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32mD:\\Anaconda\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3791\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3790\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3791\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3792\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[1;32mindex.pyx:152\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mindex.pyx:181\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'policy_state '",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[13], line 7\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m col1:\n\u001b[0;32m      6\u001b[0m     ax\u001b[38;5;241m=\u001b[39mplt\u001b[38;5;241m.\u001b[39msubplot(\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m4\u001b[39m,j)\n\u001b[1;32m----> 7\u001b[0m     ax\u001b[38;5;241m=\u001b[39mplt\u001b[38;5;241m.\u001b[39mscatter(x\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(train)),y\u001b[38;5;241m=\u001b[39mtrain[ col],color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mred\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      8\u001b[0m     plt\u001b[38;5;241m.\u001b[39mtitle(col)\n\u001b[0;32m      9\u001b[0m     j\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m\n",
      "File \u001b[1;32mD:\\Anaconda\\Lib\\site-packages\\pandas\\core\\frame.py:3893\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3891\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   3892\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3893\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[0;32m   3894\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m   3895\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[1;32mD:\\Anaconda\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3798\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3793\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m   3794\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[0;32m   3795\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[0;32m   3796\u001b[0m     ):\n\u001b[0;32m   3797\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[1;32m-> 3798\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3799\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3800\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3801\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3802\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3803\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: 'policy_state '"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col1=[ 'policy_state ', 'insured_sex', 'insured_education_level ', 'insured_relationship' , 'incident_type',\\\n",
    "       'incident_state', 'auto_make ']\n",
    "plt.figure(figsize=(20,10))\n",
    "j=1\n",
    "for col in col1:\n",
    "    ax=plt.subplot(4,4,j)\n",
    "    ax=plt.scatter(x=range(len(train)),y=train[ col],color='red')\n",
    "    plt.title(col)\n",
    "    j+=1\n",
    "k=9\n",
    "for col in col1:\n",
    "    ax=plt.subplot(4,4,k)\n",
    "    ax=plt.scatter(x=range(len(test)),y=test[col],color='cyan ')\n",
    "    plt.title(col)\n",
    "    k+=1\n",
    "plt.subplots_adjust(wspace=0.4,hspace=0.3)#润整图间距\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cac2999-77a1-4b2d-a78f-0f25170faa01",
   "metadata": {},
   "source": [
    "<b>数据探索-相关性分析</b><br>\n",
    "<b>5.相关性分析</b><br>\r",
    "- 观察数据的特征之间是否存在相关性，以便判断是否存在冗余特征，<br>\n",
    "或者观察特征和目标变量之间是否相关性，以便为特征工程提供依据。<br>\r",
    "- 相关性是数据不同特征之间相关关系的度量，也即一个特征的取值随着<br>\n",
    "另外一个特征取值的变化情况。常用的相关性度量方法包括：<br>\n",
    "协方差、皮尔逊 （Pearson）相关系数、斯皮尔曼（Spearman）相关系数、肯德尔（Kendall）相关系数等<br>\n",
    "\n",
    "\n",
    "使用pandas包中的corr()方法计算相关系数，<br>\n",
    "使用seaborn包中的heatmap方法绘制热力图<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f25b749d-c19f-46d5-b57e-260ae9188ce0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#过滤数值数据,此处为第一次查看，后续将娄别型数据转换后会进行进一步分析\n",
    "correlation_matrix=train[ Nu_feature].corr()\n",
    "plt.figure(figsize=(12,10))\n",
    "sns.heatmap(correlation_matrix, vmax=0.9,linewidths=0.05,cmap=\"RdGy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d065bc5a-b8fd-443b-961f-fb13f61d1449",
   "metadata": {},
   "source": [
    "- 数值型特征<br>\n",
    "his()用于画数值特征的频数直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e691d736-ba3c-4ba8-9d7c-d2b862679937",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iyJAh+R4FAFq8VhkbpaWlUVFREeXl5fkeBQBavFYZGwDAliM2AICkxAYAkJTYAACSEhsAQFJiAwBISmwAAEmJDQAgKbEBACQlNgCApMQGAJCU2AAAkhIbAEBSYgMASEpsAABJiQ0AICmxAQAkJTYAgKTEBgCQlNgAAJISGwBAUmIDAEhKbAAASYkNACApsQEAJNUqY6OsrCxKSkpiyJAh+R4FAFq8VhkbpaWlUVFREeXl5fkeBQBavFYZGwDAliM2AICkxAYAkJTYAACSEhsAQFJiAwBISmwAAEmJDQAgKbEBACQlNgCApMQGAJCU2AAAkhIbAEBSYgMASEpsAABJiQ0AICmxAQAkJTYAgKTEBgCQlNgAAJISGwBAUmIDAEhKbAAASYkNACApsQEAJCU2AICkxAYAkJTYAACSEhsAQFJiAwBISmwAAEm1ytgoKyuLkpKSGDJkSL5HAYAWr1XGRmlpaVRUVER5eXm+RwGAFq9VxgYAsOWIDQAgKbEBACQlNgCApMQGAJCU2AAAkhIbAEBSYgMASEpsAABJiQ0AICmxAQAkJTYAgKTEBgCQlNgAAJISGwBAUmIDAEhKbAAASYkNACApsQEAJCU2AICkxAYAkJTYAACSEhsAQFJiAwBISmwAAEmJDQAgKbEBACQlNgCApMQGAJCU2AAAkhIbAEBSYgMASEpsAABJiQ0AICmxAQAkJTYAgKTEBgCQlNgAAJISGwBAUmIDAEiqVcZGWVlZlJSUxJAhQ/I9CgC0eK0yNkpLS6OioiLKy8vzPQoAtHitMjYAgC1HbAAASYkNACApsQEAJCU2AICkxAYAkJTYAACSEhsAQFIF+R4AAFqSysrKqK6uzvcY6+nRo0f07t07L88tNgCgmVRWVsbeew+MlStX5HuU9XTqtF3Mmzc3L8EhNgCgmVRXV8fKlSviG2OvjMLivvkep07Nkndj5n1XR3V1tdgAgJagsLhvdO89IN9jbDXcIAoAJCU2AICkxAYAkJTYAACSEhsAQFJiAwBISmwAAEmJDQAgKbEBACQlNgCApMQGAJCU2AAAkhIbAEBSYgMASEpsAABJiQ0AICmxAQAkJTYAgKTEBgCQlNgAAJISGwBAUmIDAEhKbAAASYkNACApsQEAJFWQ7wFga1dZWRnV1dX5HmM9PXr0iN69e+d7DICvJDZgIyorK2PvvQfGypUr8j3Kejp12i7mzZsrOICtntiAjaiuro6VK1fEN8ZeGYXFffM9Tp2aJe/GzPuujurqarEBbPXEBmyCwuK+0b33gHyPAbBNcoMoAJCU2AAAkhIbAEBSYgMASEpsAABJiQ0AICmxAQAkJTYAgKTEBgCQlNgAAJISGwBAUmIDAEhKbAAASYkNACApsQEAJCU2AICkxAYAkFRBvgfIh7KysigrK4vPP/88IiJqamqa7diffvppRER8vmplrFm5vNmO2xw+X7UyIv4+Y3Oec0u2tX4/fS9bjq11jUVYZ02xtX4/U3wv1x0ny7Kv3DeXbcpeLdRf//rX6NWrV77HAIBt1qJFi2K33Xbb6D6tOjbWrl0bixcvju233z5yuVyzHLOmpiZ69eoVixYtisLCwmY5Zkvh2jTMddkw16ZhrsuGuTYNS3FdsiyLZcuWRc+ePaNNm43fldEq30ZZp02bNl9ZY01VWFhooW+Aa9Mw12XDXJuGuS4b5to0rLmvS9euXTdpPzeIAgBJiQ0AICmx0cw6dOgQV155ZXTo0CHfo2x1XJuGuS4b5to0zHXZMNemYfm+Lq36BlEAID2vbAAASYkNACApsQEAJCU2AICkxEYjzZgxI4477rjo2bNn5HK5ePzxx7/ya6ZPnx4HHnhgdOzYMfbYY4+466670g+6hTX2ukybNi1yudx6H/PmzdsyA28h1157bQwZMiS233772HnnneP444+Pt99++yu/rjWsmaZcm9awbu68884YNGhQ3S9fGjp0aPzxj3/c6Ne0hvUS0fhr0xrWS0OuvfbayOVycd555210vy25bsRGIy1fvjz222+/uOOOOzZp/4ULF8YxxxwT3/rWt2LOnDnxs5/9LMaNGxePPvpo4km3rMZel3XefvvtWLJkSd1H//79E02YH9OnT4/S0tJ45ZVXYsqUKfH555/HiBEjYvnyDf+BptayZppybdZpyetmt912i+uuuy5mzZoVs2bNiiOOOCJGjRoVb731VoP7t5b1EtH4a7NOS14vX1ZeXh533313DBo0aKP7bfF1k9FkEZFNnjx5o/tcdNFF2d57713vsTPPPDM7+OCDE06WX5tyXaZOnZpFRPbxxx9vkZm2FlVVVVlEZNOnT9/gPq1xzWTZpl2b1rpuunXrlt1zzz0Nbmut62WdjV2b1rZeli1blvXv3z+bMmVKNmzYsGz8+PEb3HdLrxuvbCT28ssvx4gRI+o9NnLkyJg1a1asWbMmT1NtPQ444IAoLi6O4cOHx9SpU/M9TnJLly6NiIju3btvcJ/WumY25dqs01rWTW1tbTz88MOxfPnyGDp0aIP7tNb1sinXZp3Wsl5KS0vj2GOPjSOPPPIr993S66ZV/yG2LeH999+PoqKieo8VFRXF559/HtXV1VFcXJynyfKruLg47r777jjwwANj1apV8eCDD8bw4cNj2rRp8e1vfzvf4yWRZVmcf/75ceihh8bXvva1De7XGtfMpl6b1rJu3njjjRg6dGh89tln0aVLl5g8eXKUlJQ0uG9rWy+NuTatZb1ERDz88MPx6quvRnl5+Sbtv6XXjdjYAr785+uz//9LW5vrz9pviwYMGBADBgyo+3zo0KGxaNGiuPHGG1vcfwTWOeecc+L111+PF1544Sv3bW1rZlOvTWtZNwMGDIjXXnstPvnkk3j00UdjzJgxMX369A3+o9qa1ktjrk1rWS+LFi2K8ePHxzPPPBMdO3bc5K/bkuvG2yiJ7bLLLvH+++/Xe6yqqioKCgpixx13zNNUW6eDDz443nnnnXyPkcS5554bTzzxREydOjV22223je7b2tZMY65NQ1riumnfvn3069cvBg8eHNdee23st99+cdtttzW4b2tbL425Ng1pietl9uzZUVVVFQceeGAUFBREQUFBTJ8+PX75y19GQUFB1NbWrvc1W3rdeGUjsaFDh8Z//ud/1nvsmWeeicGDB0e7du3yNNXWac6cOS3uJd8sy+Lcc8+NyZMnx7Rp02L33Xf/yq9pLWumKdemIS1x3XxZlmWxatWqBre1lvWyIRu7Ng1pietl+PDh8cYbb9R77Ac/+EHsvffecfHFF0fbtm3X+5otvm6S3Hbagi1btiybM2dONmfOnCwisptvvjmbM2dO9pe//CXLsiy75JJLstNOO61u/z//+c/Zdtttl/30pz/NKioqsnvvvTdr165d9vvf/z5fp5BEY6/LLbfckk2ePDn73//93+zNN9/MLrnkkiwiskcffTRfp5DEWWedlXXt2jWbNm1atmTJkrqPFStW1O3TWtdMU65Na1g3EyZMyGbMmJEtXLgwe/3117Of/exnWZs2bbJnnnkmy7LWu16yrPHXpjWslw358k+j5HvdiI1GWvejVF/+GDNmTJZlWTZmzJhs2LBh9b5m2rRp2QEHHJC1b98+69u3b3bnnXdu+cETa+x1uf7667M999wz69ixY9atW7fs0EMPzf7whz/kZ/iEGromEZHdf//9dfu01jXTlGvTGtbN2LFjsz59+mTt27fPdtppp2z48OF1/5hmWetdL1nW+GvTGtbLhnw5NvK9bvyJeQAgKTeIAgBJiQ0AICmxAQAkJTYAgKTEBgCQlNgAAJISGwBAUmIDtgKHHXZYnHfeefkeo0Hz5s2Lgw8+ODp27Bj7779/8ud74IEHYocddtjoPmeccUYcf/zxm3S8d999N3K5XLz22mubPdtXacxczWXatGmRy+Xik08+Sfo8+Tg3Wg5/GwXYqCuvvDI6d+4cb7/9dnTp0iXf40RExG233RZb4+8j3FrngnwTG9BC1dbWRi6XizZtNu8FzAULFsSxxx4bffr0aabJNl/Xrl3zPUKDtta5IN+8jQJfcNhhh8W4cePioosuiu7du8cuu+wSV111VUQ0/HL8J598ErlcLqZNmxYR//eS9tNPPx0HHHBAdOrUKY444oioqqqKP/7xjzFw4MAoLCyMU089NVasWFHvuT///PM455xzYocddogdd9wxLrvssnr/L3n16tVx0UUXxa677hqdO3eOb3zjG3XPG/F/bz/813/9V5SUlESHDh3iL3/5y0bPd+3atTFx4sTYbbfdokOHDrH//vvHU089Vbc9l8vF7NmzY+LEiZHL5equxYYMHTo0LrnkknqPffDBB9GuXbuYOnXqJp3HOk8//XQMHDgwunTpEkcddVQsWbKkbtuXX9Jfu3ZtXH/99dGvX7/o0KFD9O7dO37xi19scM6Kioo45phjokuXLlFUVBSnnXZaVFdX123//e9/H/vuu2906tQpdtxxxzjyyCNj+fLlGz33huba2HqKiDj11FPjlFNOqXeMNWvWRI8ePeL++++PiIhVq1bFuHHjYuedd46OHTvGoYceGuXl5Q0+/9KlS6NTp071vocREY899lh07tw5Pv3004iIeO+99+Lkk0+Obt26xY477hijRo2Kd999t27/2traOP/88+vW4kUXXeQVGzaL2IAv+c1vfhOdO3eOmTNnxg033BATJ06MKVOmNOoYV111Vdxxxx3x0ksvxaJFi+K73/1u3HrrrTFp0qT4wx/+EFOmTInbb799vectKCiImTNnxi9/+cu45ZZb4p577qnb/oMf/CBefPHFePjhh+P111+Pk046KY466qh455136vZZsWJFXHvttXHPPffEW2+9FTvvvPNG57ztttvipptuihtvvDFef/31GDlyZPzjP/5j3TGXLFkS++yzT1xwwQWxZMmS+Nd//deNHm/06NHxu9/9rt4/TI888kgUFRXFsGHDGnUeN954Yzz44IMxY8aMqKys3OhzT5gwIa6//vq4/PLLo6KiIiZNmhRFRUUN7rtkyZIYNmxY7L///jFr1qx46qmn4m9/+1t897vfrdt+6qmnxtixY2Pu3Lkxbdq0OPHEE5v8j+3G1tPo0aPjiSeeqIuAiL9H1vLly+Of/umfIiLioosuikcffTR+85vfxKuvvhr9+vWLkSNHxkcffbTec3Xt2jWOPfbYeOihh+o9PmnSpBg1alR06dIlVqxYEYcffnh06dIlZsyYES+88EJd0K1evToiIm666aa477774t57740XXnghPvroo5g8eXKTzh8iwp+Yhy8aNmxYduihh9Z7bMiQIdnFF1+cLVy4MIuIbM6cOXXbPv744ywisqlTp2ZZ9n9//fbZZ5+t2+faa6/NIiJbsGBB3WNnnnlmNnLkyHrPO3DgwGzt2rV1j1188cXZwIEDsyzLsvnz52e5XC5777336s02fPjwbMKECVmWZdn999+fRUT22muvbfL59uzZM/vFL36x3vmeffbZdZ/vt99+2ZVXXrlJx6uqqsoKCgqyGTNm1D02dOjQ7MILL2z0ecyfP79ue1lZWVZUVFT3+ZgxY7JRo0ZlWZZlNTU1WYcOHbJf//rXDc705e/b5Zdfno0YMaLePosWLcoiInv77bez2bNnZxGRvfvuu5t0zl/0xbmybOPrKcuybPXq1VmPHj2y3/72t3XbTz311Oykk07KsizLPv3006xdu3bZQw89VLd99erVWc+ePbMbbrghy7L/W3Mff/xxlmVZ9thjj2VdunTJli9fnmVZli1dujTr2LFj3V87vffee7MBAwbUW2urVq3KOnXqlD399NNZlmVZcXFxdt1119VtX7NmTbbbbrvVOzdoDK9swJcMGjSo3ufFxcVRVVXV5GMUFRXFdtttF3vssUe9x758zIMPPjhyuVzd50OHDo133nknamtr49VXX40sy2KvvfaKLl261H1Mnz49FixYUPc17du3X2/+DampqYnFixfHIYccUu/xQw45JObOnduo811np512iu985zt1/8964cKF8fLLL8fo0aMjIjb5PLbbbrvYc8896z7f2Pdg7ty5sWrVqhg+fPgmzTh79uyYOnVqveffe++9I+Lv96fst99+MXz48Nh3333jpJNOil//+tfx8ccfN+l6RGx8PbVr1y5OOumkuuu1fPny+I//+I+667VgwYJYs2ZNve9Ru3bt4qCDDtrg9+jYY4+NgoKCeOKJJyIi4tFHH43tt98+RowYUXf+8+fPj+23377u/Lt37x6fffZZLFiwIJYuXRpLliyJoUOH1h2zoKAgBg8e3ORrAG4QhS9p165dvc9zuVysXbu27kbL7Asvp69Zs+Yrj5HL5TZ4zE21du3aaNu2bcyePTvatm1bb9sXf0KkU6dO9YJlU3x5/yzLGn2MLxo9enSMHz8+br/99pg0aVLss88+sd9++0XEpp9HQ9cr28DbGJ06dWrUfGvXro3jjjsurr/++vW2FRcXR9u2bWPKlCnx0ksvxTPPPBO33357XHrppTFz5szYfffdG/VcERteT+uMHj06hg0bFlVVVTFlypTo2LFjHH300RHxf2utMd+j9u3bxz//8z/HpEmT4pRTTolJkybFySefHAUFBXXnf+CBB673VkvE32MRUvDKBmyidf8h/uKNis35uxteeeWV9T7v379/tG3bNg444ICora2Nqqqq6NevX72PXXbZpUnPV1hYGD179owXXnih3uMvvfRSDBw4sMnncfzxx8dnn30WTz31VEyaNCm+//3v121LcR79+/ePTp06xXPPPbdJ+3/961+Pt956K/r27bveDJ07d46Iv//jfsghh8TVV18dc+bMifbt2ye7Z+Gb3/xm9OrVKx555JF46KGH4qSTTor27dtHRES/fv2iffv29b5Ha9asiVmzZm30ezR69Oh46qmn4q233oqpU6fWvVKy7vzfeeed2Hnnndc7/65du0bXrl2juLi43nr8/PPPY/bs2QnOntZCbMAm6tSpUxx88MFx3XXXRUVFRcyYMSMuu+yyZjv+okWL4vzzz4+33347fve738Xtt98e48ePj4iIvfbaK0aPHh2nn356PPbYY7Fw4cIoLy+P66+/Pp588skmP+eFF14Y119/fTzyyCPx9ttvxyWXXBKvvfZa3fM2RefOnWPUqFFx+eWXx9y5c+N73/te3bYU59GxY8e4+OKL46KLLorf/va3sWDBgnjllVfi3nvvbXD/0tLS+Oijj+LUU0+N//7v/44///nP8cwzz8TYsWOjtrY2Zs6cGddcc03MmjUrKisr47HHHosPPvhgswJsY3K5XHzve9+Lu+66K6ZMmVIvzjp37hxnnXVWXHjhhfHUU09FRUVF/PjHP44VK1bED3/4ww0ec9iwYVFUVBSjR4+Ovn37xsEHH1y3bfTo0dGjR48YNWpUPP/887Fw4cKYPn16jB8/Pv76179GRMT48ePjuuuui8mTJ8e8efPi7LPPTv5Lw2jZvI0CjXDffffF2LFjY/DgwTFgwIC44YYb6t4L31ynn356rFy5Mg466KBo27ZtnHvuufEv//Ivddvvv//++PnPfx4XXHBBvPfee7HjjjvG0KFD45hjjmnyc44bNy5qamriggsuiKqqqigpKYknnngi+vfvv1nnMnr06Dj22GPj29/+dvTu3bvethTncfnll0dBQUFcccUVsXjx4iguLo6f/OQnDe7bs2fPePHFF+Piiy+OkSNHxqpVq6JPnz5x1FFHRZs2baKwsDBmzJgRt956a9TU1ESfPn3ipptuqntrI4XRo0fHNddcE3369FnvHprrrrsu1q5dG6eddlosW7YsBg8eHE8//XR069Ztg8fL5XJx6qmnxr/927/FFVdcUW/bdtttFzNmzIiLL744TjzxxFi2bFnsuuuuMXz48CgsLIyIqPvpozPOOCPatGkTY8eOjRNOOCGWLl3a/CdPq5DLNvRGKABAM/A2CgCQlNiAFuyLP9755Y/nn3++0ce75pprNni8lG8zbC2a+3pCa+FtFGjB5s+fv8Ftu+66a6N/bPSjjz5q8DdXRvz9Btpdd921Ucfb1jT39YTWQmwAAEl5GwUASEpsAABJiQ0AICmxAQAkJTYAgKTEBgCQlNgAAJISGwBAUv8PgVhGo9I+sToAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#字故车翎强分布\n",
    "sns.histplot(train['number_of_vehicles_involved'])\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f0db35b0-d419-4fb5-9dc2-fc4ec69940bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#斯诈和正常情况下整体索赔金额分布\n",
    "frauds = train[train.fraud == 1]\n",
    "normal = train[train.fraud == 0]\n",
    "f,(ax1,ax2) = plt.subplots(2,1, sharex=True)\n",
    "bins = 50\n",
    "ax1.hist(frauds.total_claim_amount,bins = bins)\n",
    "ax1.set_title('欺诈')\n",
    "ax2.hist(normal.total_claim_amount,bins = bins)\n",
    "ax2.set_title('正常')\n",
    "plt.xlabel( 'total_claim_amount ($)')\n",
    "plt.ylabel( '事务数')\n",
    "plt.xlim((0,130000))\n",
    "plt.yscale( 'log' )\n",
    "plt.show( );\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9422d8d1-4585-4a26-a7c1-95fa4bf23cd7",
   "metadata": {},
   "source": [
    "- 类别特征<br>\n",
    "sns.countplot() 用于画类别特征的频数条形图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "eb853ad0-f528-4b44-a496-668830a6e371",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(4,3,figsize=(15,15))\n",
    "train['fraud_str'] = train['fraud'].astype(str)\n",
    "sns.countplot(data=train, x='policy_state', hue='fraud_str' ,ax=ax[0][0])\n",
    "ax[0][0].set_title('上保险所在地区')\n",
    "sns.countplot(data=train,x='insured_sex' , hue='fraud_str', ax=ax[0][1])\n",
    "ax[0][1].set_title('被保人性别')\n",
    "sns.countplot(data=train,x='insured_education_level' , hue='fraud_str', ax=ax[0][2])\n",
    "ax[0][2].set_title('被保人学历')\n",
    "sns.countplot(data=train,x='insured_occupation' , hue='fraud_str' , ax=ax[1][0])\n",
    "ax[1][0].set_title('被保人职业')\n",
    "sns.countplot(data=train,x= 'bodily_injuries' , hue='fraud_str', ax=ax[1][1])\n",
    "ax[1][1].set_title('身体伤害')\n",
    "sns.countplot(data=train,x='auto_make' , hue='fraud_str' , ax=ax[1][2])\n",
    "ax[1][2].set_title('汽车品牌')\n",
    "sns.countplot(data=train,x='insured_hobbies' , hue='fraud_str' , ax=ax[2][0])\n",
    "ax[2][0].set_title('被保人兴趣爱好')\n",
    "sns.countplot(data=train,x='insured_relationship' , hue='fraud_str' , ax=ax[2][1])\n",
    "ax[2][1].set_title('被保人关系')\n",
    "sns.countplot(data=train,x='incident_type' , hue='fraud_str' , ax=ax[2][2])\n",
    "ax[2][2].set_title('出险类型')\n",
    "sns.countplot(data=train,x='collision_type' , hue='fraud_str', ax=ax[3][0])\n",
    "ax[3][0].set_title('碰撞类型')\n",
    "sns.countplot(data=train,x='incident_severity', hue='fraud_str' , ax=ax[3][1])\n",
    "ax[3][1].set_title('事故严重程度')\n",
    "sns.countplot(data=train,x= 'authorities_contacted', hue= 'fraud_str', ax=ax[3][2])\n",
    "ax[3][2].set_title('联系了当地的哪个机构')\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76b1a865-b967-4e48-9180-c3a030b9d316",
   "metadata": {},
   "source": [
    "<b>特征工程</b><br>\r",
    "基于现有特征，通过一定的规则或算法生成新的特征。<br>\n",
    "这需要对业务场景有深入的理解，才能构造出有实际意义的特征。<br>\n",
    "<b>(1) 统计量构造</b><br>\r",
    "- 使用常用的统计量构造特征，常用的统计量有：<br>\r",
    "四分位数、中位数、平均值、标准差、偏差、偏度、偏锋、离散系统<br>\r",
    "- 通过加大时间周期构造<br>\r",
    "例如周和月，统计更长周期的数据作为特征。<br>\r",
    "- 权重<br>\r",
    "通过时间衰减组合新特征。（越靠近观测权重值高）<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a560e00-56bb-43ed-8cc5-37a380614f30",
   "metadata": {},
   "source": [
    "(2) 周期特征\n",
    "前n个周期/天/月/年的周期值，如过去5天分位数、平均值等\n",
    "同比/环比\n",
    "(3) 特征组合\n",
    "可以将几个不同的特征进行组合，新的特征也许能够更好的表征数据，优先考虑强特征维度，有大概一下几种组合方式：\n",
    "离散+离散：笛卡尔积\n",
    "离散+连续：连续特征分桶后进行笛卡尔积或基于类别特征 group by，类似于聚类特征构造\n",
    "连续+连续：加减乘除，二阶差分等"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c36be64-1e69-4cb4-8da1-19e2b32f1fde",
   "metadata": {},
   "source": [
    "(4）数学变换\r",
    "基础数学变换，如两个特征相除，或者几个特征进行复杂数据运算\r",
    "对数变换\r",
    "    对于具有重尾分布的正数值的处理，对数变换是一个非常强大的工具。（与高斯分布相比，重尾分布的概率质量更多地位于尾部。）它压缩了分布高端的长尾，使之成为较短的尾部，并将低端扩展为更长的头部。\r",
    "指数变换\r",
    "    指数变换是个变换族，对数变换只是它的一个特例。用统计学术语来说，它们都是方差稳定化变换。\r"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f6c44df-4c58-49f7-a98c-15eb271c0dbd",
   "metadata": {},
   "source": [
    "(5) 特征拆解\n",
    "对于一些字符串，可以通过分解，得到一些特征\n",
    "\n",
    "ID numbers: '123-45-6789'\n",
    "\n",
    "Phone numbers: '(999) 555-0123'\n",
    "\n",
    "Street addresses: '8241 Kaggle Ln., Goose City, NV'\n",
    "\n",
    "Internet addresses: 'http://www.kaggle.com\n",
    "\n",
    "Product codes: '0 36000 29145 2'\n",
    "\n",
    "Dates and times: 'Mon Sep 30 07:06:05 2013'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "272630c4-01b2-4398-9b3c-c5fc891e283b",
   "metadata": {},
   "source": [
    "(6) 新特征发现\r",
    "有时候，可以通过已有的数据，发现一些新的特征，这个特征可以更加明确的帮助进行分类，想要发现新的特征，需要做到如下：\r",
    "理解特征\r",
    "去了解一些关于数据的领域知识，这些知识可以帮你更好的了解特征\r",
    "研究前人的工作，帮助开阔思路\r",
    "使用数据可视化，可以帮助直观的观察数据\r",
    "(7) 自动构建发现\r",
    "基于python 和sklearn，有一个称为gplearn的库，可以遗传算法和符号回归，自动的去探索各种可能的特征组合，从而发现更好的特征。\r"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b562254d-315b-4e2b-945a-ad9deb27231f",
   "metadata": {},
   "source": [
    "特征选择也称为“属性选择”或“变量选择”，是指在构建数据挖掘模型之前，选择重要特征子集的过程。\r",
    "特征是否发散：如果一个特征不发散，例如方差接近于0，也就是说样本在这个特征上基本上没有差异，这个特征对于样本的区分并没有什么用。\r",
    "特征与目标的相关性：这点比较显见，与目标相关性高的特征，应当优选选择。除方差法外，本文介绍的其他方法均从相关性考虑。\r"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70e937ad-badc-4f67-b715-76031f5ceb7a",
   "metadata": {},
   "source": [
    "特征选择的目的：\n",
    "降低过拟合风险，提升模型效果\n",
    "提高训练速度，降低运算开销\n",
    "更少的特征通常意味着更好的可解释性"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae032172-a94d-4ff0-b98c-44b03dd3766b",
   "metadata": {},
   "source": [
    "过滤法（Filter）：特征选择独立于数据挖掘任务，按照特征的发散程度或者特征与目标变量之间的相关性对各个特征进行评分，然后设定阈值选出评分较高的特征子集。\n",
    "包装法（Wrapper）：特征选择和数据挖掘算法相关，直接使用数据挖掘模型在特征子集上评价结果衡量该子集的优劣，然后采用一定的启发式方法在特征空间中搜索，直至选择出最优的特征子集。\n",
    "嵌入法（Embedded）：特征选择和数据挖掘任务融为一体，两者在同一个优化过程中完成，也即，在训练数据挖掘模型的同时完成特征选择，选择出能够使得该模型性能达到最佳的特征子集。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "be49654c-164b-4ca2-990e-fde587585553",
   "metadata": {},
   "source": [
    "评估指标——混淆矩阵与准确率指标\r",
    "F值（F Score）:准确率和召回率的调和平均值\r",
    "      计算方法为2*(Accurancy*Recall)/(Accurancy+Recall)\r",
    "      如果一个模型的准确率为0，召回率为1，那么F值仍为0\r",
    "ROC曲线和AUC值:构建了很多组混淆矩阵\r",
    "在有些模型的产出中，通常给出“是”和“否”的概率值（这两个概率值相加为1），根据概率值来判定最终的结果。把预测结果归类为正样本的阈值为多少，决定了最后的预测结果\r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e5c9a30-d4d8-4938-9df0-7a7bc13c12f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "评估指标——混淆矩阵与准确率指标\n",
    "\n",
    "在每一组混淆矩阵中，获取两个值：\n",
    "真正例率：TP/(TP+FN)\n",
    "假正例率：FP/(FP+TN)\n",
    "ROC曲线：以真正例率TPR为纵轴，以假正例率FPR为横轴，在不同的阈值下获得坐标点，并连接各个坐标点，得到ROC曲线\n",
    "AUC(Area under Curve)：ROC曲线下的面积，介于0.1和1之间。AUC作为数值可以直观的评价分类器的好坏，值越大越好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26179333-deff-452d-8783-ca30d9edd613",
   "metadata": {},
   "outputs": [],
   "source": [
    "sklearn包包含多种评估指标计算方法：\n",
    "准确率：accuracy_score(y_true, y_pred)\n",
    "精确率：precision_score(y_true, y_pred)\n",
    "召回率：recall_score(y_true, y_pred)\n",
    "F1:f1_score(y_true, y_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e13c4db4-1822-4455-9ed6-6fc16907da4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_scorefrom sklearn.metrics import precision_scorefrom sklearn.metrics import recall_scorefrom sklearn.metrics import f1_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "_train，x_val， y_train， y_val = train_test_split(x_train_2,y_train_2, test_size=0.3，random_state=42)\n",
    "model_lgb = lgb. LGBMClassifier(verbosity= -1)\n",
    "#模型训练，适用炭认参数\n",
    "model_lgb.fit(x_train,y_train)\n",
    "# AUC平测:proba进行提交，结巢会更好y_pred = model_lgb.predict(x_val)\n",
    "print(\"准确率为:\", accuracy_score(y_val,y_pred))print(\"精确率为: \"， precision_score(y_val，y_pred))print(“召回率为: \", recall_score(y_val, y_pred))print(\"F1为:\",f1_score(y _val, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4e3c217-bf5e-43eb-8080-812d1a358004",
   "metadata": {},
   "outputs": [],
   "source": [
    "K折交叉验证（K-fold cross-validation）是一种常用的模型评估技术，用于评估模型在训练数据上的性能。它将原始数据集分成K个子集，称为折（fold）。然后，模型在K个不同的训练集上进行K次训练和验证，在每次训练中，其中一个折被作为验证集，而剩下的K-1个折被用作训练集。这样就产生了K个模型和对应的K个验证分数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "889deaa3-9f31-4431-adef-79b0118f0ad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifierfrom sklearn.model_selection import KFold\n",
    "import lightgbm as lgb\n",
    "from sklearn.metrics import roc_auc_score,roc_curve, auc\n",
    "#5折交叉验证\n",
    "kf = KFold(n_splits = 5, shuffle=True，random_state=o)score=o\n",
    "for train_index, test_index in kf.split(x_train_2):\n",
    "x_train，x_test = x_train_2。ilot[train_index]， x_train_2[test_index]y_train，y_test = y_train_2[train_index],y_train_2[test_index]clf = lgb.LGBMClassifier(verbosity= -1).fit(x_train， y_train)test_auc = roc_auc_score(y_test,clf.predict_proba(x_test)[:,1])print(\"AUC为: \", test_auc)\n",
    "score = score + test_auc\n",
    "avg_auc = score / 5\n",
    "print(\"平均AUC为: \", avg_auc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "218ceacf-738d-4bca-8553-9f8e25772e91",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_scorefrom sklearn.model_selection import KFold\n",
    "#初绐化模型\n",
    "model_1gb = lgb.LGBMClassifier(verbosity= -1)\n",
    "#将数据集划分为5个折，对数据进行洗牌，并设翌厨机种子为ekf = KFold(n_splits=5， shuffle=True，random_state=)\n",
    "lgb_scores = cross_val_score(model_1gb，x_train_2， y_train_2，cv=kf ,scoring='roc_auc \")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78f27600-cd3b-4b13-9885-7c1be69b91d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "模型参数调优\n",
    "\n",
    "learning_rate：学习率\n",
    "n_estimators：拟合的树的棵树，可以理解为训练的轮数\n",
    "max_depth：最大树的深度\n",
    "objective ：目标，二分类还是多分类，“regression”、“binary”\n",
    "num_leaves：叶子节点数量\n",
    "random_state：随机种子数\n",
    "reg_alpha：L1正则化系数\n",
    "verbose:是否显示训练过程中的详细信息，-1表示不显示\n",
    "random_state是一个随机种子，是在任意带有随机性的类或函数里作为参数来控制随机模式。当random_state取某一个值时，也就确定了一种规则\n",
    "GridSearch网格搜索：\n",
    "GridSearch网格搜索是一种穷举搜索的参数调优手段：遍历所有的候选参数，循环建立模型并评估模型的有效性和准确性，选取表现最好的参数作为最终结果。\n",
    "\n",
    "RandomizedSearchCV随机搜索调参：\n",
    "与网格搜索相比，随机搜索并未尝试所有参数值，而是从指定的分布中采样固定数量的参数设置。它的理论依据是，如果随即样本点集足够大，那么也可以找到全局的最大或最小值，或它们的近似值。对于规模较大的参数空间，采用随机搜索往往效率更高\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32089c7e-e8e2-4bd3-b818-21d94f39c787",
   "metadata": {},
   "outputs": [],
   "source": [
    "#划分数据\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train，x_val，y_train， y_val = train_test_split(x_train_2,y_train_2, test_size=0.3， random_ state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66dde150-a3ad-4d51-9bce-dc7fe93dce8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb\n",
    "from sklearn.model_selection import RandomizedsearchCv\n",
    "param_grid = {\n",
    "\"num_leaves': range(20，150)，#叶子节点数运\n",
    "'learning_rate ': [e.005,0.01,8.05，0.1]，#学习率'n_estimators ' : [ 1500,1600,1700,1800,190o, 2000]\n",
    "}\n",
    "#初始化模型\n",
    "model = lgb.LGBNClassifier(objective='binary' , verbose=-1， random_state=2024)\n",
    "#使用篙机占索进行参数澜优\n",
    "random_search = RandomizedSearchCv(estimator=model,\n",
    "param_distributions=param_grid，#参数组合n_iter=100,\n",
    "cv=5，#5折交又验证verbose=2,\n",
    "random_state=42,n_jobs=-1)\n",
    "#模型湖练\n",
    "random_search.fit(×_train, y_train)\n",
    "#禽出最锉参数\n",
    "print(\"Best parameters found: \", random_search.best_params_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc22ae9b-6893-45af-b014-5a405133e398",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score#使用最住参数湖练模型\n",
    "best_model = random_search.best_estimator_best_model.fit(X_train,y_train)\n",
    "#预测\n",
    "y_prob = best_model.predict_proba(x_val)[:,1]\n",
    "#平估模型\n",
    "print ( ' AUC: %.4f’%roc_auc score(y_val, y_prob\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0963a3d8-52d9-4b01-8ea2-0a3963406d1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import Gridsearchcvparam_grid = {\n",
    "\"num_leaves ' : [20,30,40],#叶子节点数兹\n",
    "'learning_rate ' : [e.005,0.01，0.05,0.1]，#学习率'n_estimators ' : [ 1500,1700,190o,2000]\n",
    "#初绐化模塑\n",
    "model = lgb.LGBNClassifier(objective='binary' , verbose=-1，random_state=2024)\n",
    "#使用网落整索进行参数两优\n",
    "grid_search = GridSearchCV(estimator=model，param_grid=param_grid, cv=5， scoring='roc_auc '， verbose=1)\n",
    "#行网格淡素\n",
    "grid_search.fit(×_train， y_train)\n",
    "#输出最锉参数\n",
    "print( \"Best parameters found: \", grid_search.best_params_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4da672ee-1e8e-4754-9fc1-03927ea3f642",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_scorebest_model = grid_search.best_estimator_best_model.fit(×_train, y_train)\n",
    "#预测\n",
    "y_prob = best_model.predict_proba(x_val)[:,1]#评估模型\n",
    "print( 'AUC: %.4f’%roc_auc_score(y_val, y_prob))\n"
   ]
  }
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